Random code shifting based ultra-wideband photonic compressive receiver with image-frequency distinction.

We propose an ultra-wideband photonic compressive receiver based on random codes shifting with image-frequency distinction. By shifting the center frequencies of two random codes in large frequency range, the receiving bandwidth is flexibly expanded. Simultaneously, the center frequencies of two random codes are slightly different. This difference is used to distinguish the "fixed" true RF signal from the differently located image-frequency signal. Based on this idea, our system solves the problem of limited receiving bandwidth of existing photonic compressive receivers. In the experiments, with two channels of only 780-MHz outputs, the sensing capability in the range of 11-41 GHz has been demonstrated. A multi-tone spectrum and a sparse radar-communication spectrum, composed of a linear frequency modulated (LFM) signal, a quadrature phase-shift keying (QPSK) signal and a single-tone signal, are both recovered.

Photonic-assisted space-frequency two-dimensional compressive radar receiver for high-resolution and wide-range detection.

Existing photonic compressive receivers have the problem of resolution deterioration when applied in wide-range radar detection. In this study, we propose a photonic-assisted space-frequency two-dimensional (2D) compressive radar receiver capable of achieving high-resolution detection in wide-range scenarios. For the space dimension, the compression process is realized by employing a spatially adaptive photonic projection basis, which guarantees complete mapping of arbitrarily delayed echoes-the key to high-resolution wide-range detection. For the frequency dimension, photonic compressive sensing is employed to further compress the bandwidth of the projected sparse signal. Therefore, the proposed system can achieve wide-range radar detection without resolution deterioration with compressed output. Herein, with two channels of 630 MHz outputs, high-resolution distance detection within a range of 21 km with a resolution of up to 2.3 cm is achieved. Moreover, inverse synthetic aperture radar (ISAR) imaging of two sets of four-point turntables distributed within the range of 21 km with a resolution of 2.3 cm × 5.7 cm is realized. The proposed photonic-assisted 2D compressive radar receiver is a viable solution to overcome the tradeoff between detection resolution and range of existing photonic compressive receivers, which indicates a path for the further development of high-resolution wide-range radar detection.

Bivariate small-area estimation for binary and gaussian variables based on a conditionally specified model.

Many large-scale surveys collect both discrete and continuous variables. Small-area estimates may be desired for means of continuous variables, proportions in each level of a categorical variable, or for domain means defined as the mean of the continuous variable for each level of the categorical variable. In this paper, we introduce a conditionally specified bivariate mixed-effects model for small-area estimation, and provide a necessary and sufficient condition under which the conditional distributions render a valid joint distribution. The conditional specification allows better model interpretation. We use the valid joint distribution to calculate empirical Bayes predictors and use the parametric bootstrap to estimate the mean squared error. Simulation studies demonstrate the superior performance of the bivariate mixed-effects model relative to univariate model estimators. We apply the bivariate mixed-effects model to construct estimates for small watersheds using data from the Conservation Effects Assessment Project, a survey developed to quantify the environmental impacts of conservation efforts. We construct predictors of mean sediment loss, the proportion of land where the soil loss tolerance is exceeded, and the average sediment loss on land where the soil loss tolerance is exceeded. In the data analysis, the bivariate mixed-effects model leads to more scientifically interpretable estimates of domain means than those based on two independent univariate models.

Efficient estimation and prediction for the Bayesian binary spatial model with flexible link functions.

Spatial generalized linear mixed models (SGLMMs) are popular models for spatial data with a non-Gaussian response. Binomial SGLMMs with logit or probit link functions are often used to model spatially dependent binomial random variables. It is known that for independent binomial data, the robit regression model provides a more robust (against extreme observations) alternative to the more popular logistic and probit models. In this article, we introduce a Bayesian spatial robit model for spatially dependent binomial data. Since constructing a meaningful prior on the link function parameter as well as the spatial correlation parameters in SGLMMs is difficult, we propose an empirical Bayes (EB) approach for the estimation of these parameters as well as for the prediction of the random effects. The EB methodology is implemented by efficient importance sampling methods based on Markov chain Monte Carlo (MCMC) algorithms. Our simulation study shows that the robit model is robust against model misspecification, and our EB method results in estimates with less bias than full Bayesian (FB) analysis. The methodology is applied to a Celastrus Orbiculatus data, and a Rhizoctonia root data. For the former, which is known to contain outlying observations, the robit model is shown to do better for predicting the spatial distribution of an invasive species. For the latter, our approach is doing as well as the classical models for predicting the disease severity for a root disease, as the probit link is shown to be appropriate. Though this article is written for Binomial SGLMMs for brevity, the EB methodology is more general and can be applied to other types of SGLMMs. In the accompanying R package geoBayes, implementations for other SGLMMs such as Poisson and Gamma SGLMMs are provided.

Spatially explicit downscaling and projection of population in mainland China.

Spatially explicit population data is critical to investigating human-nature interactions, identifying at-risk populations, and informing sustainable management and policy decisions. Most long-term global population data have three main limitations: 1) they were estimated with simple scaling or trend extrapolation methods which are not able to capture detailed population variation spatially and temporally; 2) the rate of urbanization and the spatial patterns of settlement changes were not fully considered; and 3) the spatial resolution is generally coarse. To address these limitations, we proposed a framework for large-scale spatially explicit downscaling of populations from census data and projecting future population distributions under different Shared Socio-economic Pathways (SSP) scenarios with the consideration of distinctive changes in urban extent. We downscaled urban and rural population separately and considered urban spatial sprawl in downscaling and projection. Treating urban and rural populations as distinct but interconnected entities, we constructed a random forest model to downscale historical populations and designed a gravity-based population potential model to project future population changes at the grid level. This work built a new capacity for understanding spatially explicit demographic change with a combination of temporal, spatial, and SSP scenario dimensions, paving the way for cross-disciplinary studies on long-term socio-environmental interactions.

Equating NHANES Monitor-Based Physical Activity to Self-Reported Methods to Enhance Ongoing Surveillance Efforts.

PURPOSE: Harmonization of assessment methods represents an ongoing challenge in physical activity research. Previous research has demonstrated the utility of calibration approaches to enhance agreement between measures of physical activity. The present study utilizes a calibration methodology to add behavioral context from the Global Physical Activity Questionnaire (GPAQ), an established report-based measure, to enhance interpretations of monitor-based data scored using the novel Monitor Independent Movement Summary (MIMS) methodology. METHODS: Matching data from the GPAQ and MIMS were obtained from adults (20-80 yr of age) assessed in the 2011-2014 National Health and Nutrition Examination Survey. After developing percentile curves for self-reported activity, a zero-inflated quantile regression model was developed to link MIMS to estimates of moderate to vigorous physical activity (MVPA) from the GPAQ. RESULTS: Cross-validation of the model showed that it closely approximated the probability of reporting MVPA across age and activity-level segments, supporting the accuracy of the zero-inflated model component. Validation of the quantile regression component directly corresponded to the 25%, 50%, and 75% values for both men and women, further supporting the model fit. CONCLUSIONS: This study offers a method of improving activity surveillance by translating accelerometer signals into interpretable behavioral measures using nationally representative data. The model provides accurate estimates of minutes of MVPA at a population level but, because of the bias and error inherent in report-based measures of physical activity, is not suitable for converting or interpreting individual-level data. This study provides an important preliminary step in utilizing information from both device- and report-based methods to triangulate activity related outcomes; however additional measurement error modeling is needed to improve precision.

Veterinarian perceptions and practices in prevention and control of influenza virus in the Midwest United States swine farms.

Influenza A virus (IAV) is an endemic respiratory pathogen affecting swine worldwide and is a public health concern as a zoonotic pathogen. Veterinarians may respond to IAV infection in swine with varied approaches depending on their perception of its economic impact on human and animal health. This study considered three primary veterinary practice categories: swine exclusive veterinary practitioner, large animal practitioner, which corresponds to veterinarians that work predominantly with food animals including but not exclusively porcine, and mixed animal practitioner, which corresponds to veterinarians working with companion and food animals. This survey aimed to assess U.S. veterinarian perceptions, biosecurity practices, and control methods for IAV in swine. In this study, 54.5% (188/345) of the veterinarians that were targeted responded to all portions of the survey. The study results presented different perceptions regarding IAV among veterinarians in different types of veterinary practices and the current IAV mitigation practices implemented in swine farms based on strategic decisions. Collectively, this study also revealed the veterinarians' perceptions that IAV as a health problem in swine is increasing, IAV has a moderate economic impact, and there is a high level of concern regarding IAV circulating in swine. These findings highlight the need for IAV surveillance data, improved vaccine strategies, as well as important opportunities regarding methods of control and biosecurity. Additionally, results of this survey suggest biosecurity practices associated with the veterinarian's swine operations and prevention of zoonotic diseases can be strengthened through annual IAV vaccination of humans and support of sick leave policies for farm workers.

Effects of Perceptual Learning on Deprivation Amblyopia in Children with Limbal Dermoid: A Randomized Controlled Trial.

Limbal dermoid (LD) is a congenital ocular tumor that causes amblyopia and damages visual acuity (VA) and visual function. This study evaluated the therapeutic efficacy of perceptual learning (PL) toward improving contrast sensitivity function (CSF) and VA. A total of 25 children with LD and 25 normal children were compared in terms of CSF and VA. The LD group was further randomly allocated into two arms: nine underwent PL combined with patching and eight underwent patching only; eight patients quit the amblyopia treatment. The primary outcome was the area under log CSF (AULCSF), and the secondary outcome was the best corrected VA (BCVA). The CSF was obviously reduced in the LD group compared with that in the normal group. Moreover, the difference in the changes in the AULCSF between the PL and patching groups after 6 months of training was 0.59 (95% CI: 0.32, 0.86, p < 0.001), and the between-group difference in VA at 6 months was −0.30 (95% CI: −0.46, −0.14, p < 0.001). Children suffering from LD with amblyopia exhibited CSF deficits and VA loss simultaneously. PL could improve CSF and VA in the amblyopic eye better than patching.

A novel clinical dynamic stereopsis assessment based on autostereoscopic display system.

Background: Although it is recognized that dynamic stereopsis is vital in daily life, there is still room for improvement in assessment methods. A novel clinical dynamic stereopsis assessment method was created based on an autostereoscopic display system that did not require additional auxiliary glasses. This study evaluated the optical parameters and clinical performance of the autostereoscopic display system for clinical dynamic stereopsis assessment. Methods: The autostereoscopic dynamic stereopsis assessment device was based on a directional backlight technology. Experiment 1 was performed under the same environmental conditions to compare luminance, crosstalk, and spectrum between the autostereoscopic dynamic stereopsis assessment device and the conventional dynamic random-dot stereopsis measuring instrument. Experiment 2 was an observational, analytic, cross-sectional study involving 135 healthy participants, each of whom was asked to complete measurements on both the autostereoscopic and conventional devices in random order. Stereo acuity, operating time, acceptance, and visual fatigue scores were recorded for clinical evaluation. Results: The autostereoscopic device had brighter luminance (139 and 140 cd/m2 for 2 eyes, respectively), lower crosstalk (4.50% for both eyes), and higher color restoration degree than those of the conventional instrument. Clinically, the novel dynamic stereopsis assessment was as accurate as the traditional method [170" (0.00") and 170" (0.00") respectively; P=0.317], and with more efficiency (166±58.9 and 298±116 s, respectively; P<0.001), higher acceptance (3.36±0.93 and 2.02±0.59 points, respectively; P<0.001), lesser fatigue (0.27±0.46 and 0.73±0.66 points, respectively; P<0.001). The autostereoscopic dynamic stereopsis assessment device with brighter luminance, lower crosstalk, and higher color restoration degree was more effective than the traditional instrument at displaying dynamic clues for clinical dynamic stereopsis assessment. Furthermore, its high-quality image and user-friendly interface provided accurate assessment results in all 3 dynamic stereopsis assessment task conditions, with a higher level of acceptance and lesser visual fatigue, than the traditional assessment method. Conclusions: The autostereoscopic device has excellent functions in both optical parameters and clinical performance, and therefore has the potential to be applied and popularized in future assessments.

Associations between ozone and morbidity using the Spatial Synoptic Classification system.

BACKGROUND: Synoptic circulation patterns (large-scale tropospheric motion systems) affect air pollution and, potentially, air-pollution-morbidity associations. We evaluated the effect of synoptic circulation patterns (air masses) on the association between ozone and hospital admissions for asthma and myocardial infarction (MI) among adults in North Carolina. METHODS: Daily surface meteorology data (including precipitation, wind speed, and dew point) for five selected cities in North Carolina were obtained from the U.S. EPA Air Quality System (AQS), which were in turn based on data from the National Climatic Data Center of the National Oceanic and Atmospheric Administration. We used the Spatial Synoptic Classification system to classify each day of the 9-year period from 1996 through 2004 into one of seven different air mass types: dry polar, dry moderate, dry tropical, moist polar, moist moderate, moist tropical, or transitional. Daily 24-hour maximum 1-hour ambient concentrations of ozone were obtained from the AQS. Asthma and MI hospital admissions data for the 9-year period were obtained from the North Carolina Department of Health and Human Services. Generalized linear models were used to assess the association of the hospitalizations with ozone concentrations and specific air mass types, using pollutant lags of 0 to 5 days. We examined the effect across cities on days with the same air mass type. In all models we adjusted for dew point and day-of-the-week effects related to hospital admissions. RESULTS: Ozone was associated with asthma under dry tropical (1- to 5-day lags), transitional (3- and 4-day lags), and extreme moist tropical (0-day lag) air masses. Ozone was associated with MI only under the extreme moist tropical (5-day lag) air masses. CONCLUSIONS: Elevated ozone levels are associated with dry tropical, dry moderate, and moist tropical air masses, with the highest ozone levels being associated with the dry tropical air mass. Certain synoptic circulation patterns/air masses in conjunction with ambient ozone levels were associated with increased asthma and MI hospitalizations.